Data center management functions, such as capacity planning, provisioning, configuring workloads, migration of workloads, and making procurement decisions, rely on performance estimates for storage systems within the data center. To provide the performance estimates, the storage systems are characterized according to certain usage models. In general, storage systems are characterized in terms of total storage capacity and transaction performance. Total storage capacity is commonly specified as a byte count (e.g., a number of terabytes of storage). Transaction performance is commonly specified as an average number of input/output (IO) operations per second (IOPS). In a given storage system, total storage tends to be very precisely characterized, while IOPS performance tends to be very poorly and imprecisely characterized. In fact, characterizing a storage system with respect to IOPS performance is a well-known, unsolved problem within the art of storage systems.
To achieve some degree of useful characterization, a storage system is typically given an IOPS specification based on an offline, static benchmark. However, conventional IOPS benchmarks do not reflect dynamic relationships that impact performance of an online system, and the benchmarks cannot easily predict performance for a diverse mix of workloads and typically only work effectively for a narrow range at a time. Furthermore, conventional benchmarks are not well configured for operation on a system servicing live traffic. A typical online system may commonly operate outside the narrow range, yielding IOPS performance that is inferior to that predicted by the static benchmarks.
In order to guarantee required overall performance without a predictive performance model, worst-case performance estimates are commonly employed as a model for storage systems in data center environments. This worst-case modeling typically results in extremely over-provisioned, over-built data center storage systems. Because storage systems oftentimes comprise a significant portion of data center capital and operations expense, over-building these systems can dramatically increase total cost of ownership of a given data center. Therefore, what is needed in the art is a technique for better characterizing storage systems with respect to IOPS performance that is operable over a broader range of operating conditions.